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Abstract #0805

Evaluation of Deep-Learning Reconstructed High-Resolution 3D Lumbar Spine MRI to Improve Image Quality

Simon Sun1, Ek Tsoon Tan1, John A Carrino1, Douglas Nelson Mintz1, Meghan Sahr1, Yoshimi Endo1, Edward Yoon1, Bin Lin1, Robert M Lebel2, Suryanarayan Kaushik2, Yan Wen2, Maggie Fung2, and Darryl B Sneag1
1Radiology, Hospital for Special Surgery, New York, NY, United States, 2GE Healthcare, Chicago, IL, United States

Advances in deep-learning algorithms aiming to improve image quality have not yet been well studied for their use in clinical interpretation. In this study, we compared interobserver agreement and image quality for lumbar spine (L-spine) MRI assessment of 3D T2-weighted fast spin echo (T2w-FSE) MRI, with and without deep learning (DLRecon) reconstructions, as well as standard-of-care (SOC) 2D T2w-FSE MRI. This pilot study demonstrated that interobserver agreement for variables of interest was good to very good regardless of reconstruction or sequence type, and overall image quality of DLRecon was not inferior despite significant reduction in scanning time.

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